This study has investigated the association between the amount of atmospheric aerosols and the occurrences of Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer in Delhi, Mumbai, Chennai, Kolkata and Bengaluru. Aerosol Optical Thickness (AOT) data of Visible Infrared Imaging Radiometer Suite (VIIRS) and Google Trends (GT) have been used to acquire information regarding the abundance of atmospheric aerosols and the occurrences of the respiratory diseases respectively. The result of Granger causality test between AOT and GT has shown that Delhi, Mumbai and Chennai were quite vulnerable to the three respiratory diseases whereas Bengaluru did not display so much vulnerability to these ailments. Kolkata was not so much vulnerable to Asthma but did exhibit susceptibility to the other two diseases. GT is validated by correlating with Annual Morbidity data of Delhi. The result of Granger causality test between Particulate Matter (diameter ≤ 10 μm) (PM10) data and GT validates the result of Granger causality between AOT and GT, and shows the trustworthiness of GT and AOT. Thus, this study also proves the usefulness of VIIRS AOT and GT as dependable sources of information on atmospheric aerosols and prevalence of the respiratory diseases respectively, and the effectiveness of Granger causality test as a tool of analysis in health and geographic information systems (GIS).

Emissions from flaring and venting (FV) in oil and gas (O&G) production are difficult to quantify due to their intermittent activities and lack of adequate monitoring and reporting. Given their potentially significant contribution to total emissions from the O&G sector in the United States, we estimate emissions from FV using Visible Infrared Imaging Radiometer Suite satellite observations and state/local reported data on flared gas volume. These refined estimates are higher than those reported in the National Emission Inventory: by up to 15 times for fine particulate matter (PM2.5), two times for sulfur dioxides, and 22% higher for nitrogen oxides (NOx). Annual average contributions of FV to ozone (O3), NO2, and PM2.5 in the conterminous U.S. (CONUS) are less than 0.15%, but significant contributions of up to 60% are found in O&G fields with FV. FV contributions are higher in winter than in summer months for O3 and PM2.5; an inverse behavior is found for NO2. Nitrate aerosol contributions to PM2.5 are highest in the Denver basin whereas in the Permian and Bakken basins, sulfate and elemental carbon aerosols are the major contributors. Over four simulated months in 2016 for the entire CONUS, FV contributes 210 additional instances of exceedances to the daily maximum 8-hr average O3 and has negligible contributions to exceedance of NO2 and PM2.5, given the current form of the national ambient air quality standards. FV emissions are found to cause over $7.4 billion in health damages, 710 premature deaths, and 73,000 asthma exacerbations among children annually.

Floating macroalgae of Sargassum horneri (S. horneri) in the East China Sea (ECS) has increased in recent years, with ocean warming being one of the driving factors. Yet their possible origins, based on a literature review, are unclear. Here, using multi-sensor high-resolution remote sensing data and numerical experiments for the period of 2015-2023, we show two possible origins of the ECS floating S. horneri, one being local near the Zhejiang coast with initiation in January-February and the other being remote (> 800 km from the first) in the Bohai Sea with initiation in June-November. While their drifting pathways are revealed in the sequential remote sensing imagery, numerical experiments suggest that S. horneri from the remote origin (Bohai Sea) can hardly meander through the strong Yangtze River frontal zone, which may serve as a \"wall\" to prevent trespassing of surface floating seaweed to the south of the frontal zone, where S. horneri has a local origin. PLAIN LANGUAGE SUMMARY: Sargassum horneri (S. horneri) is a brown macroalgae (seaweed) abundant in surface waters of the East China Sea (ECS), which can serve as a moving habitat, but can also cause major beaching events and environmental problems. Knowledge of its origins is important to help implement mitigation strategies and understand possible ecological impacts along its drifting pathways. Using high-resolution remote sensing images and numerical experiments, we track floating S. horneri in space and time between 2015 and 2023. Two possible origins are identified, one being far away from the ECS and the other being local, both of which are known to have benthic S. horneri. The study also reveals how S. horneri are transported from their source regions resulting in large-scale distributions previously observed in medium-resolution satellite imagery.

Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City\'s air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005-2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40-110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

The structure of 9-year time series data for Sea Surface Temperature (SST), Chlorophyll a (Chl-a) and Total Suspended Solids (TSS), derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), was examined in this study. Authors found that there exists strong seasonality among the three variables with spatial heterogeneity along the Korean South Coast (KSC). In specific, SST was in phase with Chl-a, but out of phase with TSS by six months. A strong inversed spectral power with six-month phase-lag was found between Chl-a and TSS. This could be attributed to different dynamics and environmental settings. For example, Chl-a concentration seemed to have strong positive correlation with SST indicating typical seasonality of marine biogeochemical processes such as primary production; while a strong negative correlation between TSS and SST might have been influenced by changes in physical oceanographic processes, such as stratification and monsoonal wind-driven vertical mixing. In addition, the strong east-west heterogeneity of Chl-a suggests that the marine coastal environments are predominantly governed by distinct local hydrological conditions and human activities associated with land cover and land use, while the east-west spatial pattern revealed in TSS timeseries was associated with the gradient of tidal forcings and topographical changes keeping tidally induced resuspension low eastward.

Hurricane Katrina (category 5 with maximum wind of 280 km/h when the eye is in the central Gulf of Mexico) made landfall near New Orleans on August 29, 2005, causing millions of cubic meters of disaster debris, severe flooding, and US$125 billion in damage. Yet, despite numerous reports on its environmental and economic impacts, little is known about how much debris has entered the marine environment. Here, using satellite images (MODIS, MERIS, and Landsat), airborne photographs, and imaging spectroscopy, we show the distribution, possible types, and amount of Katrina-induced debris in the northern Gulf of Mexico. Satellite images collected between August 30 and September 19 show elongated image features around the Mississippi River Delta in a region bounded by 92.5°W-87.5°W and 27.8°N-30.25°N. Image spectroscopy and color appearance of these image features indicate that they are likely dominated by driftwood (including construction lumber) and dead plants (e.g., uprooted marsh) and possibly mixed with plastics and other materials. The image sequence shows that if aggregated together to completely cover the water surface, the maximal debris area reached 21.7 km2 on August 31 to the east of the delta, which drifted to the west following the ocean currents. When measured by area in satellite images, this perhaps represents a historical record of all previously reported floating debris due to natural disasters such as hurricanes, floodings, and tsunamis.

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012-2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56-59% of valid retrievals with high RMSE (0.30-0.33) and bias. Overall, DB slightly underpredicted AOD with -0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.

The dark target (DT) and deep blue (DB) algorithms have been applied to the VIIRS to construct a long-term climate data recording of atmospheric aerosols. This study provides the first evaluation and comparison of two updated VIIRS aerosol products over global land based on Version 3 Level 1.5 AERONET measurements. Overall, both AOD products agree well with AERONET measurements with correlation coefficients >0.85 and over 75 % of AOD matchups falling within the expected error (EE). The DB product is superior to the DT product with more AOD matchups meeting the EE. Meanwhile, the DB AOD performs better in spatiotemporal accuracy, adaption to different aerosol types, and addresses the effects of large view geometry. DT AOD shows higher accuracy in the summer months in the northern hemisphere and evergreen broadleaf forest areas than DB AOD. The two products exhibit similar spatial distribution patterns of AOD, but higher values are seen in the DT product, especially in Asia and Western North America. The spatial completeness of the DB AOD is higher than DT, especially in brighter surface regions. In contrast, higher temporal completeness of the DT AOD is found in vegetated areas. A case study of the Western United States wildfires in 2020 indicates both products can capture extreme smoke aerosols. In addition, the DB AOD shows a distinct advantage in spatial and temporal continuity and in displaying the regional transport of smoke aerosols. However, the accuracy of the AOD retrieval for both products still needs to be improved in sparse vegetation regions, northern hemisphere summers, fine particle aerosols, and during extreme aerosol events. The overall evaluation of DB SSA and AE products shows that they are unsuitable for quantitative scientific research. This study can provide users a guide on how to select VIIRS aerosol products for different research purposes.